Abstract
Classical contextual advertising systems suggest suitable ads to a given webpage, without relying on further information – i.e. just analyzing its content. Although we agree that the target webpage is important for selecting ads, in this paper we concentrate on the importance of taking into account also information extracted from the webpages that link the target webpage (inlinks). According to this insight, contextual advertising can be viewed as a collaborative filtering process, in which selecting a suitable ad corresponds to estimate to which extent the ad matches the characteristics of the “current user” (the webpage), together with the characteristics of similar users (the inlinks). We claim that, in so doing, the envisioned collaborative approach is able to improve classical contextual advertising. Experiments have been performed comparing a collaborative system implemented in accordance with the proposed approach against (i) a classical content-based system and (ii) a system that relies only on the content of similar pages (disregarding the target webpage). Experimental results confirm the validity of the approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Armano, G., de Gemmis, M., Semeraro, G., Vargiu, E.: Intelligent Information Access. SCI, vol. 301. Springer, Heidelberg (2010)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Broder, A.Z., Ciccolo, P., Fontoura, M., Gabrilovich, E., Josifovski, V., Riedel, L.: Search advertising using web relevance feedback. In: Proc. of 17th. Int. Conference on Information and Knowledge Management, pp. 1013–1022 (2008)
Anastasakos, T., Hillard, D., Kshetramade, S., Raghavan, H.: A collaborative ltering approach to ad recommendation using the query-ad click graph. In: Proc. of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1927–1930. ACM, New York (2009)
Vargiu, E., Urru, M.: Exploiting web scraping in a collaborative ltering-based approach to web advertising. Artificial Intelligence Research 2(1), 44–54 (2013)
Armano, G., Vargiu, E.: A unifying view of contextual advertising and recommender systems. In: Proc. of Int. Conference on Knowledge Discovery and Information Retrieval (KDIR 2010), pp. 463–466 (2010)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Vargiu, E., Giuliani, A., Armano, G.: Improving contextual advertising by adopting collaborative filtering. ACM Transaction on the Web (in press, 2013)
Armano, G., Giuliani, A., Vargiu, E.: Intelligent Techniques in Recommender Systems and Contextual Advertising: Novel Approaches and Case Studies. In: Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods, pp. 105–128. IGI Global (2012)
Koolen, M., Kamps, J.: Are semantically related links more effective for retrieval? In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 92–103. Springer, Heidelberg (2011)
Armano, G., Giuliani, A., Vargiu, E.: Are related links effective for contextual advertising? a preliminary study. In: Int. Conference on Knowledge Discovery and Information Retrieval (2012)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company (1984)
Armano, G., Giuliani, A., Vargiu, E.: Using snippets in text summarization: A comparative study and an application. In: IIR 2012: 3rd Italian Information Retrieval (IIR) Workshop (2012)
Broder, A., Fontoura, M., Josifovski, V., Riedel, L.: A semantic approach to contextual advertising. In: SIGIR 2007: Proc. of the 30th annual Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 559–566. ACM, New York (2007)
Rocchio, J.: Relevance feedback in information retrieval. In: The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. PrenticeHall (1971)
Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proc. of the 23rd Int. ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2000, pp. 41–48. ACM, New York (2000)
Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proc. of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 621–630. ACM, New York (2009)
Anagnostopoulos, A., Broder, A.Z., Gabrilovich, E., Josifovski, V., Riedel, L.: Just-in-time contextual advertising. In: CIKM 2007: Proc. of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 331–340. ACM, New York (2007)
Ribeiro-Neto, B., Cristo, M., Golgher, P.B., Silva de Moura, E.: Impedance coupling in content-targeted advertising. In: SIGIR 2005: Proc. of the 28th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 496–503. ACM, New York (2005)
Lacerda, A., Cristo, M., Gonçalves, M.A., Fan, W., Ziviani, N., Ribeiro-Neto, B.: Learning to advertise. In: SIGIR 2006: Proc. of the 29th Int. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 549–556. ACM, New York (2006)
Yih, W.T., Goodman, J., Carvalho, V.R.: Finding advertising keywords on web pages. In: WWW 2006: Proc. of the 15th Int. Conference on World Wide Web, pp. 213–222. ACM, New York (2006)
Armano, G., Giuliani, A., Vargiu, E.: Semantic enrichment of contextual advertising by using concepts. In: Int. Conference on Knowledge Discovery and Information Retrieval (2011)
Liu, H., Singh, P.: Conceptnet: A practical commonsense reasoning tool-kit. BT Technology Journal 22, 211–226 (2004)
Armano, G., Giuliani, A., Vargiu, E.: Experimenting text summarization techniques for contextual advertising. In: IIR 2011: Proc. of the 2nd Italian Information Retrieval (IIR) Workshop (2011)
Armano, G., Giuliani, A., Vargiu, E.: Studying the impact of text summarization on contextual advertising. In: 8th Int. Workshop on Text-based Information Retrieval (2011)
Picard, J., Savoy, J.: Enhancing retrieval with hyperlinks: a general model based on propositional argumentation systems. Journal of the American Society for Information Science and Technology 54, 347–355 (2003)
Frei, H.P., Stieger, D.: The use of semantic links in hypertext information retrieval. Information Processing and Management 31, 1–13 (1995)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)
Chakrabarti, S., van den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific Web resource discovery. Computer Networks 31(11-16), 1623–1640 (1999)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58, 1019–1031 (2007)
Addis, A., Armano, G., Vargiu, E.: Assessing progressive filtering to perform hierarchical text categorization in presence of input imbalance. In: Proc. of Int. Conference on Knowledge Discovery and Information Retrieval, KDIR 2010 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Armano, G., Giuliani, A. (2013). Matching Ads in a Collaborative Advertising System. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-39878-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39877-3
Online ISBN: 978-3-642-39878-0
eBook Packages: Computer ScienceComputer Science (R0)